6 citations · 7 across the 4 of their papers we have counts for
5 papers
Learning functional sections in medical conversations: iterative pseudo-labeling and human-in-the-loop approach
Mengqian Wang, Ilya Valmianski, Xavier Amatriain +1
Medical conversations between patients and medical professionals have implicit functional sections, such as "history taking", "summarization", "education", and "care plan." In this…
Medically Aware GPT-3 as a Data Generator for Medical Dialogue Summarization
Bharath Chintagunta, Namit Katariya, Xavier Amatriain +1
In medical dialogue summarization, summaries must be coherent and must capture all the medically relevant information in the dialogue. However, learning effective models for summar…
Medical symptom recognition from patient text: An active learning approach for long-tailed multilabel distributions
Ali Mottaghi, Prathusha K Sarma, Xavier Amatriain +2
We study the problem of medical symptoms recognition from patient text, for the purposes of gathering pertinent information from the patient (known as history-taking). A typical pa…
Dr. Summarize: Global Summarization of Medical Dialogue by Exploiting Local Structures
Anirudh Joshi, Namit Katariya, Xavier Amatriain +1
Understanding a medical conversation between a patient and a physician poses a unique natural language understanding challenge since it combines elements of standard open ended con…
Classification As Decoder: Trading Flexibility For Control In Neural Dialogue
Sam Shleifer, Manish Chablani, Namit Katariya +2
Generative seq2seq dialogue systems are trained to predict the next word in dialogues that have already occurred. They can learn from large unlabeled conversation datasets, build a…